Content output method and apparatus, computer-readable medium, and electronic device

Abstract

Provided are a content output method and apparatus, a computer-readable medium, and an electronic device. The method includes: obtaining information of each of candidate content items for a user; predicting, based on the information of each of the candidate content items by invoking a pre-trained content recommendation model, an expected benefit for each of the candidate content items resulting from a user's operation on each of the candidate content items, in which the pre-trained content recommendation model is trained historical data of the user, and the historical data includes an actual benefit resulting from a user's operation on a historical content item and information of the historical content item; and outputting a first content item based on the expected benefit for each of the candidate content items.

1 . A content output method, comprising:
 obtaining information of each of candidate content items for a user;   predicting, based on the information of each of the candidate content items by invoking a pre-trained content recommendation model, an expected benefit for each of the candidate content items resulting from a user's operation on each of the candidate content items, the pre-trained content recommendation model being trained by historical data of the user, and the historical data comprising an actual benefit resulting from a user's operation on a historical content item, information of the historical content item, and capability attribute information of the user; and   outputting a first content item based on the expected benefit for each of the candidate content items.   
     
     
         2 . The method according to  claim 1 , further comprising:
 obtaining a first actual benefit resulting from a user's operation on the first content item;   obtaining updated historical data by updating, based on the first actual benefit, the historical data;   obtaining an updated content recommendation model by updating, based on the updated historical data, the content recommendation model;   predicting, based on information of each of remaining candidate content items except the first content item by invoking the updated content recommendation model, an expected benefit for each of the remaining candidate content items resulting from a user's operation on each of the remaining candidate content items; and   outputting a second content item based on the expected benefit for each of the remaining candidate content items.   
     
     
         3 . The method according to  claim 1 , wherein:
 the information of the candidate content item is information of a candidate question, the information of the candidate question comprising question difficulty and a predetermined question benefit, the predetermined question benefit comprising a predetermined correctness benefit and a predetermined incorrectness benefit,   said predicting, based on the information of each of the candidate content items by invoking the pre-trained content recommendation model, the expected benefit for each of the candidate content items resulting from the user's operation on each of the candidate content items comprises:   predicting, based on the information of the candidate question by means of the content recommendation model, a correctness probability, a correctness benefit, an incorrectness probability, and an incorrectness benefit for the user to answering the candidate question; and   calculating an expected benefit for the candidate question based on the correctness probability, the correctness benefit, the incorrectness probability, and the incorrectness benefit for the user to answer the candidate question.   
     
     
         4 . The method according to  claim 1 , wherein said outputting the first content item based on the expected benefit for each of the candidate content items comprises:
 determining a candidate content item with a greatest expected benefit among the candidate content items as the first content item; and   outputting the first content item.   
     
     
         5 . The method according to  claim 1 , wherein, wherein said outputting the first content item to the user based on the expected benefit for each of the candidate content items comprises:
 obtaining an operating duration required for operating each of the candidate content items by the user;   obtaining an expected benefit rate for each of the candidate content items based on the expected benefit for each of the candidate content items and the operating duration for each of the candidate content items;   determining a candidate content item with a greatest expected benefit rate among the candidate content item as the first content item; and   outputting the first content item.   
     
     
         6 . A content output apparatus, comprising:
 an obtaining module configured to obtain information of each of candidate content items for a user;   a processing module configured to predict, based on the information of each of the candidate content items by invoking a pre-trained content recommendation model, an expected benefit for each of the candidate content items resulting from a user's operation on each of the candidate content items, the pre-trained content recommendation model being trained by historical data of the user, and the historical data comprising an actual benefit resulting from a user's operation on a historical content item, information of the historical content item, and capability attribute information of the user; and   an output module configured to output a first content item based on the expected benefit for each of the candidate content items.   
     
     
         7 . The content output apparatus according to  claim 6 , wherein:
 the obtaining module is further configured to obtain a first actual benefit resulting from a user's operation on the first content item;   the processing module is further configured to: obtain updated historical data by updating, based on the first actual benefit, the historical data; obtain an updated content recommendation model by updating, based on the updated historical data, the content recommendation model; and predict, based on information of each of remaining candidate content items except the first content item by invoking the updated content recommendation model, an expected benefit for each of the remaining candidate content items resulting from a user's operation on each of the remaining candidate content items; and   the output module is further configured to output a second content item based on the expected benefit for each of the remaining candidate content items.   
     
     
         8 . The content output apparatus according to  claim 6 , wherein:
 the information of the candidate content item is information of a candidate question, the information of the candidate question comprising question difficulty and a predetermined question benefit, the predetermined question benefit comprising a predetermined correctness benefit and a predetermined incorrectness benefit; and   the processing module is further configured to: predict, based on the information of the candidate question by means of the content recommendation model, a correctness probability, a correctness benefit, an incorrectness probability, and an incorrectness benefit for the user to answer the candidate question; and calculate an expected benefit for the candidate question based on the correctness probability, the correctness benefit, the incorrectness probability, and the incorrectness benefit for the user to answer the candidate question.   
     
     
         9 . A computer-readable medium, having a computer program stored thereon, wherein the computer program, when executed by a processor causes the processor to:
 obtain information of each of candidate content items for a user;   predict, based on the information of each of the candidate content items by invoking a pre-trained content recommendation model, an expected benefit for each of the candidate content items resulting from a user's operation on each of the candidate content items, the pre-trained content recommendation model being trained by historical data of the user, and the historical data comprising an actual benefit resulting from a user's operation on a historical content item, information of the historical content item, and capability attribute information of the user; and   output a first content item based on the expected benefit for each of the candidate content items.   
     
     
         10 . An electronic device, comprising:
 a memory having a computer program stored thereon; and   a processor configured to execute the computer program stored on the memory to implement the content output method according to  claim 1 .   
     
     
         11 . The content output apparatus according to  claim 6 , wherein:
 the processing module is further configured to determine a candidate content item with a greatest expected benefit among the candidate content items as the first content item; and   the output module is further configured to output the first content item.   
     
     
         12 . The content output apparatus according to  claim 6 , wherein:
 the obtaining module is further configured to obtain an operating duration required for operating each of the candidate content items by the user;   the processing module is further configured to: obtain an expected benefit rate for each of the candidate content items based on the expected benefit for each of the candidate content items and the operating duration for each of the candidate content items; and determine a candidate content item with a greatest expected benefit rate among the candidate content item as the first content item; and   the output module is further configured to output the first content item.   
     
     
         13 . The computer-readable medium according to  claim 9 , wherein the computer program, when executed by the processor, further causes the processor to:
 obtain a first actual benefit resulting from a user's operation on the first content item;   obtain updated historical data by updating, based on the first actual benefit, the historical data;   obtain an updated content recommendation model by updating, based on the updated historical data, the content recommendation model;   predict, based on information of each of remaining candidate content items except the first content item by invoking the updated content recommendation model, an expected benefit for each of the remaining candidate content items resulting from a user's operation on each of the remaining candidate content items; and   output a second content item based on the expected benefit for each of the remaining candidate content items.   
     
     
         14 . The computer-readable medium according to  claim 9 , wherein:
 the information of the candidate content item is information of a candidate question, the information of the candidate question comprising question difficulty and a predetermined question benefit, the predetermined question benefit comprising a predetermined correctness benefit and a predetermined incorrectness benefit,   the computer program, when executed by the processor, further causes the processor to:   predict, based on the information of the candidate question by means of the content recommendation model, a correctness probability, a correctness benefit, an incorrectness probability, and an incorrectness benefit for the user to answering the candidate question; and   calculate an expected benefit for the candidate question based on the correctness probability, the correctness benefit, the incorrectness probability, and the incorrectness benefit for the user to answer the candidate question.   
     
     
         15 . The computer-readable medium according to  claim 9 , wherein the computer program, when executed by the processor, further causes the processor to:
 determine a candidate content item with a greatest expected benefit among the candidate content items as the first content item; and   output the first content item.   
     
     
         16 . The computer-readable medium according to  claim 1 , wherein the computer program, when executed by the processor, further causes the processor to:
 obtain an operating duration required for operating each of the candidate content items by the user;   obtain an expected benefit rate for each of the candidate content items based on the expected benefit for each of the candidate content items and the operating duration for each of the candidate content items;   determine a candidate content item with a greatest expected benefit rate among the candidate content item as the first content item; and   output the first content item.   
     
     
         17 . The electronic device according to  claim 10 , wherein the processor is further configured to execute the computer program stored on the memory to:
 obtain a first actual benefit resulting from a user's operation on the first content item;   obtain updated historical data by updating, based on the first actual benefit, the historical data;   obtain an updated content recommendation model by updating, based on the updated historical data, the content recommendation model;   predict, based on information of each of remaining candidate content items except the first content item by invoking the updated content recommendation model, an expected benefit for each of the remaining candidate content items resulting from a user's operation on each of the remaining candidate content items; and   output a second content item based on the expected benefit for each of the remaining candidate content items.   
     
     
         18 . The electronic device according to  claim 10 , wherein:
 the information of the candidate content item is information of a candidate question, the information of the candidate question comprising question difficulty and a predetermined question benefit, the predetermined question benefit comprising a predetermined correctness benefit and a predetermined incorrectness benefit,   said predicting, based on the information of each of the candidate content items by invoking the pre-trained content recommendation model, the expected benefit for each of the candidate content items resulting from the user's operation on each of the candidate content items comprises:   predicting, based on the information of the candidate question by means of the content recommendation model, a correctness probability, a correctness benefit, an incorrectness probability, and an incorrectness benefit for the user to answering the candidate question; and   calculating an expected benefit for the candidate question based on the correctness probability, the correctness benefit, the incorrectness probability, and the incorrectness benefit for the user to answer the candidate question.   
     
     
         19 . The electronic device according to  claim 10 , wherein said outputting the first content item based on the expected benefit for each of the candidate content items comprises:
 determining a candidate content item with a greatest expected benefit among the candidate content items as the first content item; and   outputting the first content item.   
     
     
         20 . The electronic device according to  claim 10 , wherein said outputting the first content item based on the expected benefit for each of the candidate content items comprises:
 obtaining an operating duration required for operating each of the candidate content items by the user;   obtaining an expected benefit rate for each of the candidate content items based on the expected benefit for each of the candidate content items and the operating duration for each of the candidate content items;   determining a candidate content item with a greatest expected benefit rate among the candidate content item as the first content item; and   outputting the first content item.